معرفی
Paige Brooks is an Associate Professor at the University College London AI Centre and a Turing Fellow at the Alan Turing Institute. She specializes in interpretable machine learning, probabilistic programming, and generative models with applications in environmental science and chemistry. Her work includes seasonal Arctic sea ice forecasting and molecular synthesis pathway modeling. Brooks holds a DPhil from the University of Oxford and has contributed to foundational research in Bayesian inference and sequential Monte Carlo methods.
Research highlights include collaborations on Arctic ice prediction with the British Antarctic Survey and development of the Molecule Chef framework for generating novel chemical compounds. She is also a statistical ambassador for the Royal Statistical Society, emphasizing ethical and transparent AI practices.
- Education: DPhil in Machine Learning at University of Oxford (under Frank Wood)
- Key Contributions: Probabilistic programming frameworks, interpretable ML, environmental AI
- Affiliations: Alan Turing Institute (Turing Fellow), Royal Statistical Society
Her research spans theoretical advances in generative models and practical applications in domains like climate science and drug discovery. Recent work emphasizes model reliability and generalization in dynamic environments.
- Scientific Awards: Turing Fellowship, Royal Statistical Society Statistical Ambassador
- Labs/Teams: UCL AI Centre, Turing Institute's Environmental AI group
